Trial reportJournal of translational medicine2023
Establishment and evaluation of a novel practical tool for the diagnosis of pre-sarcopenia in young people with diabetes mellitus.
Trial report in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Biomarkers for sarcopenia, muscle mass, muscle strength, and physical performance: an umbrella review.Journal of translational medicine · 2025Pooled it
- Predictive nomogram for abdominal aortic calcification risk: a multi-algorithm approach integrating demographic and clinical variables.Journal of cardiothoracic surgery · 2026Article
- Development of a machine learning model for predicting pre-sarcopenia in adults with abdominal obesity: a cross-sectional study.Journal of endocrinological investigation · 2026Article
- From mouth to muscle: mechanistic and interventional perspectives on the tongue-coating microbiome in sarcopenia.Journal of translational medicine · 2026Review
- Risk prediction of sarcopenia in a large health checkup population: development and validation of a dynamic online nomogram.Frontiers in public health · 2026Article
- Association between the ratio of uric acid to high-density lipoprotein cholesterol (UHR) and the abnormal risk of sarcopenia: Evidence from two large population-based surveys and interpretable machine learning-driven sarcopenia screening.Therapeutic advances in endocrinology and metabolism · 2026Article
- Development and validation of a Four-Year predictive model for sarcopenia in older adults: insights from the CHARLS cohort.Aging clinical and experimental research · 2025Article
- Sarcopenia Is Associated With Increased Risk of Abnormal Sleep Duration in the Older People: A 10-Year Cohort Study From China.Journal of cachexia, sarcopenia and muscle · 2025Article
- Association between sleep duration and hip fracture risk among the older adults: a cross-sectional study based on the NHANES.BMC musculoskeletal disorders · 2025Article
- Association between oral microbiome and sleep disorders in U.S. adults: analysis of NHANES database 2009-2012.BMC oral health · 2025Article
- Quantifying Suicide Risk in Prostate Cancer: A SEER-Based Predictive Model.Journal of epidemiology and global health · 2025Article
- Development and validation of a nomogram for arthritis: a cross-sectional study based on the NHANES.Scientific reports · 2025Article
- Development of a clinical nomogram for predicting sarcopenia in patients with chronic obstructive pulmonary disease based on NHANES data.Frontiers in medicine · 2025Article
- Development and validation of a predictive model for the risk of possible sarcopenia in middle-aged and older adult diabetes mellitus in China.Frontiers in public health · 2025Article
- Sarcopenia prediction model based on machine learning and SHAP values for community-based older adults with cardiovascular disease in China.Frontiers in public health · 2025Article
- Construct prediction models for low muscle mass with metabolic syndrome using machine learning.PloS one · 2025Article
- A w-ACT model for sarcopenia among community-dwelling older adults based on National Basic Public Health Services: development and validation study.Frontiers in public health · 2025Article
- Individual and joint association of Life's Essential 8 metrics with pre-sarcopenia among U.S. adults.BMC musculoskeletal disorders · 2024Article
- Association between sleep duration and sleep quality with pre-sarcopenia in the 20-59-year-old population: evidence from the National Health and Nutrition Examination Surveys 2005-2014.Archives of public health = Archives belges de sante publique · 2024Article
- Machine learning prediction of no reflow in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention.Cardiovascular diagnosis and therapy · 2024Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveSarcopenia has been recognized as a third category of complications in people with diabetes. However, few studies focus on the reduction of skeletal muscle mass in young people with diabetes. The aim of this study was to investigate risk factors of pre-sarcopenia in young patients with diabetes and establish a practical tool to diagnose pre-sarcopenia in those people.
methodsPatients (n = 1246) enrolled from the National Health and Nutrition Examination Survey (NHANES) cycle year of 2011 to 2018 were randomly divided into the training set and validation set. The all-subsets regression analysis was used to select the risk factors of pre-sarcopenia. A nomogram model for the prediction of pre-sarcopenia in the diabetic population was established based on the risk factors. The model was evaluated by the area under the receiver operating characteristic curve for discrimination, calibration curves for calibration, and decision curve analysis curves for clinical utility.
resultsIn this study, gender, height, and waist circumference were elected as predictive factors for pre-sarcopenia. The nomogram model presented excellent discrimination in training and validation sets with areas under the curve of 0.907 and 0.912, respectively. The calibration curve illustrated excellent calibration, and the decision curve analysis showed a wide range of good clinical utility.
conclusionsThis study develops a novel nomogram that integrates gender, height, and waist circumference and can be used to easily predict pre-sarcopenia in diabetics. The novel screen tool is accurate, specific, and low-cost, highlighting its potential value in clinical application.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.